1 Introduction

In this evaluation, there are total 1 data tables. We used the evaluation metrics implemented in OmicsEV package to evaluate these data tables. The sample and class information for each data table are shown in the table below.

class paper
Basal 11
Her2 8
LumA 12
LumB 18
None 14

The detailed sample information is shown below.

sample class batch order
TCGA.AO.A12D None 1 1
TCGA.C8.A131 Basal 1 2
TCGA.AO.A12B None 1 3
TCGA.E2.A10A LumA 1 4
TCGA.C8.A130 LumB 1 5
TCGA.C8.A138 Her2 1 6
TCGA.E2.A154 LumA 1 7
TCGA.A8.A09I LumB 1 8
TCGA.C8.A12L Her2 1 9
TCGA.A2.A0EX LumA 1 10
TCGA.AN.A04A None 1 11
TCGA.BH.A0AV Basal 1 12
TCGA.A2.A0D0 Basal 1 13
TCGA.C8.A12T Her2 1 14
TCGA.A8.A06Z LumB 1 15
TCGA.A2.A0D1 None 1 16
TCGA.A2.A0CM Basal 1 17
TCGA.A2.A0YI LumA 1 18
TCGA.A2.A0EQ Her2 1 19
TCGA.AR.A0TY LumB 1 20
TCGA.AR.A0U4 None 1 21
TCGA.BH.A0HP LumA 1 22
TCGA.BH.A0EE Her2 2 23
TCGA.AO.A0J9 None 2 24
TCGA.AN.A0FK LumA 2 25
TCGA.AO.A0J6 None 2 26
TCGA.A7.A13F LumB 2 27
TCGA.A7.A0CE Basal 2 28
TCGA.A2.A0YC LumA 2 29
TCGA.AO.A0JC None 2 30
TCGA.AR.A0TX Her2 2 31
TCGA.D8.A13Y LumB 2 32
TCGA.A8.A076 LumB 2 33
TCGA.AO.A126 None 2 34
TCGA.C8.A12P Her2 2 35
TCGA.BH.A0C1 LumA 2 36
TCGA.A2.A0EY LumB 2 37
TCGA.AR.A1AW LumB 2 38
TCGA.AR.A1AV LumA 2 39
TCGA.C8.A135 Her2 2 40
TCGA.A2.A0EV LumA 2 41
TCGA.AN.A0AM LumB 2 42
TCGA.D8.A142 Basal 2 43
TCGA.AN.A0FL Basal 3 44
TCGA.AN.A0AS LumA 3 45
TCGA.AR.A0TV LumB 3 46
TCGA.C8.A12Z Her2 3 47
TCGA.AO.A0JJ None 3 48
TCGA.AO.A0JE None 3 49
TCGA.A2.A0T2 Basal 3 50
TCGA.AN.A0AJ LumB 3 51
TCGA.A7.A0CJ LumB 3 52
TCGA.AO.A12F None 3 53
TCGA.A2.A0YL LumA 3 54
TCGA.A2.A0T7 LumA 3 55
TCGA.C8.A12Q Her2 3 56
TCGA.A8.A079 LumB 3 57
TCGA.E2.A159 Basal 3 58
TCGA.A2.A0T3 LumB 3 59
TCGA.A2.A0YD LumA 3 60
TCGA.AR.A0TR LumA 3 61
TCGA.AO.A03O None 3 62
TCGA.AO.A12E None 3 63
TCGA.A8.A06N LumB 3 64
TCGA.A2.A0T1 Her2 3 65
TCGA.A2.A0YG LumB 3 66
TCGA.E2.A150 Basal 3 67
TCGA.A7.A0CD LumA 4 68
TCGA.C8.A12W LumB 4 69
TCGA.AN.A0AL Basal 4 70
TCGA.A2.A0T6 LumA 4 71
TCGA.AO.A0JM None 4 72
TCGA.C8.A12V Basal 4 73
TCGA.A2.A0D2 Basal 4 74
TCGA.C8.A12U LumB 4 75
TCGA.A8.A09G Her2 4 76
TCGA.C8.A134 Basal 4 77
TCGA.A2.A0YF LumA 4 78
TCGA.BH.A0E9 LumA 4 79
TCGA.AR.A0TT LumB 4 80
TCGA.AR.A1AQ Basal 4 81
TCGA.A2.A0SW LumB 4 82
TCGA.AO.A0JL None 4 83
TCGA.A2.A0YM Basal 4 84
TCGA.BH.A0C7 LumB 4 85
TCGA.A2.A0SX Basal 4 86

2 Overview

The table below provides an overview about all the quantitative metrics generated in the evaluation. For each metric, the value of the best data table is highlighted. The detail of each metric can be found in corresponding section below.

metric paper
#identified features 10062
(0.4936)
#quantifiable features 9227
(0.4526)
non_missing_value_ratio 0.9397
data_dist_similarity 0.9188
silhouette_width -0.4237
(0.5763)
pcRegscale 0.0000
(1.0000)
complex_auc 0.7368
func_auc 0.8630
class_auc 0.7418
gene_wise_cor 0.3784
sample_wise_cor 0.1783

The radar plot showing below is generated based on the data in the above overview table. To generate the radar plot, a metric is converted to a scale in which the value range is between 0 and 1 in a way that higher value indicates better data quality if necessary. The converted values are in parentheses.

3 Data depth

3.1 Study-wise

The table below shows the number of identified proteins or genes for each data table. We take the proteins or genes filtered by 50% missing value as quantified proteins or genes. The values in parentheses are the percentage of proteins or genes identified or quantified based on the total number of proteins or genes (20386) in the study species.

data table #identified features #quantifiable features
paper 10062
(49.36%) 		9227
(45.26%)

3.2 Sample-wise

The figures below show the number of proteins or genes identified in each sample. Only when the quantification value of a gene or protein is not “NA” in a sample, this gene or protein is considered as identified in the sample. The samples from different batches are coded in different shapes and the samples from different classes are coded in different colors.

paper

3.3 Missing value distribution

The missing value distribution can give an overview of the percent of missing values of all proteins or genes in both the QC and experiment samples.

data table non_missing_value_ratio
paper 0.9397

paper

4 Data normalization

4.1 Boxplot

The boxplots show the protein or gene expression distribution across samples. X axis is sample ordered by input order. Y axis is log2 transformed protein or gene expression. The samples from different classes are coded in different colors.

paper

To quantify the normalization effect, for each pair of samples, perform an AUROC test to quantify the ability of feature abundance to distinguish the two samples and then generate a score based on 1-2*abs(AUROC-0.5), which will be 0 to 1, higher the better (no systematic difference between the two samples). The final metric for each data table is the median of scores from all sample pairs.

data table data_dist_similarity n
paper 0.9188 1953

4.2 Density plot

The density plots show the protein or gene expression distribution across samples. X axis is log2 transformed protein or gene expression. Y axis is density.

5 Batch effect

5.1 Silhouette width

The silhouette width s(i) ranges from –1 to 1, with s(i) -> 1 if two clusters are separate and s(i) -> −1 if two clusters overlap but have dissimilar variance. If s(i) -> 0, both clusters have roughly the same structure. Thus, we use the absolute value |s| as an indicator for the presence or absence of batch effects.

data table silhouette_width
paper -0.4237

5.2 PCA with batch annotation

For each PC, we calculate Pearson’s correlation coefficient with batch covariate b:

ri =corr(PCi,b)

In a linear model with a single dependent, as is the case here for the PCs correlated to batch covariate, the coefficient of determination R2 is the squared Pearson’s correlation coefficient:

R2(PCi,b) = ri2

Then we estimate the significance of the correlation coefficient either with a t-test or a one-way ANOVA. The R2 value highlighted with red is significant (p-value <= 0.05).

PC paper
1 0.007
2 0.044
3 0.004
4 0.018
5 0.001
6 0.053
7 0.034
8 0
9 0.001
10 0.009

The fraction of variance explained for each PC:

PC paper
1 11.6
2 8.2
3 7.2
4 4.0
5 4.0
6 3.4
7 2.6
8 2.4
9 2.3
10 2.3

‘Scaled PC regression’, i.e. total variance of PCs which correlate significantly with batch covariate (FDR<0.05) scaled by the total variance of 10 PCs:

data table pcRegscale
paper 0

In these figures, each column is a sample, each row is also a sample. The color indicates the correlation between samples. The samples are ordered by batches.

5.3 Correlation heatmap

In these figures, each column is a sample, each row is also a sample. The color indicates the correlation between samples. The samples are ordered by batches.

paper

6 Biological signal

6.1 Correlation among protein complex members

The table showing below is a summary of the evaluation. ‘diff’ is Cor(intra) - Cor(inter). ‘complex_auc’ is the AUROC value based on correlation of protein pairs from different groups.

data table InterComplex IntraComplex diff complex_auc
paper 0.0156 0.2157 0.2002 0.7368
RNA 0.0188 0.1465 0.1277 0.6571

6.2 Gene function prediction

In this evaluation, each data table was used to build co-expression network. For a selected network and a selected function term (such as GO or KEGG), proteins/genes annotated to the term and also included in the network were defined as a positive protein/gene set and other proteins/genes in the network constituted the negative protein/gene set for the term. For a selected function term, we use some of the proteins/genes as the seed protein/gene, then we use random walk algorithm to calculate scores for other proteins/genes. A higher score of a protein/gene represents a closer relationship between the protein/gene and the seed proteins/genes. Finally, for each selected function term, we calculate an AUROC to evaluate the prediction performance.

paper RNA
ABC transporters 0.762 0.735
Acute myeloid leukemia 0.802 0.507
Adherens junction 0.781 0.633
Adipocytokine signaling pathway 0.751 0.596
Alanine, aspartate and glutamate metabolism 0.66 0.603
Aldosterone-regulated sodium reabsorption 0.862 0.574
Allograft rejection 0.938 0.986
Alzheimers disease 0.781 0.719
Amino sugar and nucleotide sugar metabolism 0.744 0.619
Aminoacyl-tRNA biosynthesis 0.779 0.746
Amoebiasis 0.791 0.723
Amyotrophic lateral sclerosis (ALS) 0.662 0.622
Antigen processing and presentation 0.849 0.845
Arachidonic acid metabolism 0.705 0.601
Arginine and proline metabolism 0.648 0.604
Arrhythmogenic right ventricular cardiomyopathy (ARVC) 0.837 0.64
Autoimmune thyroid disease 0.922 0.986
Axon guidance 0.708 0.604
B cell receptor signaling pathway 0.735 0.543
Bacterial invasion of epithelial cells 0.756 0.588
Base excision repair 0.667 0.712
beta-Alanine metabolism 0.745 0.694
Bile secretion 0.811 0.658
Biosynthesis of unsaturated fatty acids 0.884 0.684
Bladder cancer 0.616 0.523
Butanoate metabolism 0.673 0.62
Calcium signaling pathway 0.729 0.552
Carbohydrate digestion and absorption 0.945 0.741
Cardiac muscle contraction 0.873 0.691
Cell adhesion molecules (CAMs) 0.805 0.796
Cell cycle 0.79 0.743
Chagas disease (American trypanosomiasis) 0.765 0.538
Chemokine signaling pathway 0.799 0.588
Chronic myeloid leukemia 0.62 0.612
Citrate cycle (TCA cycle) 0.937 0.817
Colorectal cancer 0.586 0.614
Complement and coagulation cascades 0.899 0.903
Cysteine and methionine metabolism 0.845 0.617
Cytokine-cytokine receptor interaction 0.686 0.762
Cytosolic DNA-sensing pathway 0.673 0.561
Dilated cardiomyopathy 0.785 0.594
DNA replication 0.711 0.84
Drug metabolism - cytochrome P450 0.756 0.748
Drug metabolism - other enzymes 0.674 0.673
ECM-receptor interaction 0.832 0.832
Endocytosis 0.619 0.526
Endometrial cancer 0.762 0.557
Epithelial cell signaling in Helicobacter pylori infection 0.624 0.591
ErbB signaling pathway 0.797 0.518
Ether lipid metabolism 0.647 0.657
Fatty acid elongation in mitochondria 0.925 0.756
Fatty acid metabolism 0.766 0.629
Fc epsilon RI signaling pathway 0.809 0.529
Fc gamma R-mediated phagocytosis 0.784 0.534
Focal adhesion 0.813 0.656
Fructose and mannose metabolism 0.903 0.595
Galactose metabolism 0.775 0.653
Gap junction 0.775 0.6
Gastric acid secretion 0.885 0.601
Glioma 0.695 0.615
Glutathione metabolism 0.748 0.608
Glycerolipid metabolism 0.593 0.726
Glycine, serine and threonine metabolism 0.799 0.623
Glycolysis / Gluconeogenesis 0.822 0.626
Glyoxylate and dicarboxylate metabolism 0.863 0.695
GnRH signaling pathway 0.693 0.603
Graft-versus-host disease 0.936 0.999
Hedgehog signaling pathway 0.787 0.57
Hematopoietic cell lineage 0.741 0.743
Hepatitis C 0.761 0.61
Histidine metabolism 0.658 0.592
Huntingtons disease 0.833 0.743
Hypertrophic cardiomyopathy (HCM) 0.785 0.595
Inositol phosphate metabolism 0.645 0.585
Insulin signaling pathway 0.779 0.582
Jak-STAT signaling pathway 0.693 0.615
Leishmaniasis 0.754 0.619
Leukocyte transendothelial migration 0.799 0.608
Long-term depression 0.79 0.618
Long-term potentiation 0.823 0.555
Lysine degradation 0.693 0.583
Lysosome 0.682 0.561
Malaria 0.725 0.74
MAPK signaling pathway 0.679 0.567
Melanogenesis 0.891 0.676
Melanoma 0.669 0.562
Metabolic pathways 0.713 0.603
Metabolism of xenobiotics by cytochrome P450 0.881 0.751
mTOR signaling pathway 0.76 0.568
N-Glycan biosynthesis 0.743 0.753
Natural killer cell mediated cytotoxicity 0.764 0.627
Neurotrophin signaling pathway 0.644 0.56
Nicotinate and nicotinamide metabolism 0.692 0.615
NOD-like receptor signaling pathway 0.678 0.562
Non-small cell lung cancer 0.759 0.562
Notch signaling pathway 0.637 0.592
One carbon pool by folate 0.616 0.708
Oocyte meiosis 0.769 0.549
Osteoclast differentiation 0.764 0.586
Oxidative phosphorylation 0.888 0.823
p53 signaling pathway 0.567 0.628
Pancreatic cancer 0.649 0.601
Pancreatic secretion 0.807 0.586
Parkinsons disease 0.895 0.801
Pathogenic Escherichia coli infection 0.785 0.627
Pathways in cancer 0.689 0.555
Pentose and glucuronate interconversions 0.78 0.665
Peroxisome 0.737 0.59
Phagosome 0.768 0.668
Phosphatidylinositol signaling system 0.677 0.607
Porphyrin and chlorophyll metabolism 0.618 0.599
PPAR signaling pathway 0.723 0.622
Primary immunodeficiency 0.881 0.83
Prion diseases 0.833 0.704
Progesterone-mediated oocyte maturation 0.794 0.614
Propanoate metabolism 0.924 0.649
Prostate cancer 0.611 0.55
Protein digestion and absorption 0.843 0.859
Protein export 0.933 0.845
Protein processing in endoplasmic reticulum 0.751 0.743
Pyrimidine metabolism 0.682 0.586
Pyruvate metabolism 0.865 0.611
Regulation of actin cytoskeleton 0.805 0.617
Renal cell carcinoma 0.735 0.623
Rheumatoid arthritis 0.75 0.652
Ribosome 0.978 0.834
RIG-I-like receptor signaling pathway 0.724 0.635
RNA transport 0.626 0.651
Salivary secretion 0.764 0.642
Shigellosis 0.756 0.511
Small cell lung cancer 0.575 0.626
SNARE interactions in vesicular transport 0.776 0.711
Sphingolipid metabolism 0.666 0.62
Staphylococcus aureus infection 0.923 0.922
Starch and sucrose metabolism 0.804 0.672
Systemic lupus erythematosus 0.953 0.812
T cell receptor signaling pathway 0.677 0.522
Terpenoid backbone biosynthesis 0.761 0.696
TGF-beta signaling pathway 0.684 0.64
Tight junction 0.842 0.55
Toll-like receptor signaling pathway 0.637 0.572
Toxoplasmosis 0.697 0.568
Tryptophan metabolism 0.738 0.612
Type I diabetes mellitus 0.904 0.954
Type II diabetes mellitus 0.635 0.648
Tyrosine metabolism 0.803 0.794
Ubiquitin mediated proteolysis 0.645 0.653
Valine, leucine and isoleucine degradation 0.766 0.721
Vascular smooth muscle contraction 0.853 0.59
Vasopressin-regulated water reabsorption 0.76 0.614
VEGF signaling pathway 0.712 0.533
Vibrio cholerae infection 0.676 0.587
Viral myocarditis 0.844 0.73
Wnt signaling pathway 0.707 0.602

paper

6.3 Sample class prediction

For each data table, machine learning models are built to predict sample class:LumA,LumB. In OmicsEV, random forest models are built and the models are evaluated using repeated 5 fold cross validation (20 times).

dataSet mean_ROC median_ROC sd_ROC
paper 0.7418 0.7442 0.0174
RNA 0.9894 0.9904 0.0037

6.4 PCA with sample class annotation

paper

6.5 Unsupervised clustering

paper

7 Multi-omics concordance

7.1 Gene-wise mRNA-protein correlation

data table n n5 n6 n7 n8 gene_wise_cor
paper 8893 2824 1508 541 70 0.3784

paper

7.2 Sample-wise mRNA-protein correlation

data table sample_wise_cor
paper 0.1783